cc_gbif.RdRemoves or flags records within 0.5 degree radius around the GBIF headquarters in Copenhagen, DK.
cc_gbif(x, lon = "decimallongitude", lat = "decimallatitude", species = "species", buffer = 1000, geod = TRUE, verify = FALSE, value = "clean", verbose = TRUE)
| x | data.frame. Containing geographical coordinates and species names. |
|---|---|
| lon | character string. The column with the longitude coordinates. Default = “decimallongitude”. |
| lat | character string. The column with the latitude coordinates. Default = “decimallatitude”. |
| species | character string. The column with the species identity. Only required if verify = TRUE. |
| buffer | numerical. The buffer around the GBIF headquarters, where records should be flagged as problematic. Units depend on geod. Default = 100 m. |
| geod | logical. If TRUE the radius is calculated based on a sphere, buffer is in meters. If FALSE the radius is calculated in degrees. Default = T. |
| verify | logical. If TRUE records are only flagged if they are the only record in a given species flagged close to a given reference. If FALSE, the distance is the only criterion |
| value | character string. Defining the output value. See value. |
| verbose | logical. If TRUE reports the name of the test and the number of records flagged. |
Depending on the ‘value’ argument, either a data.frame
containing the records considered correct by the test (“clean”) or a
logical vector (“flagged”), with TRUE = test passed and FALSE = test failed/potentially
problematic . Default = “clean”.
Not recommended if working with records from Denmark or the Copenhagen area.
See https://ropensci.github.io/CoordinateCleaner for more details and tutorials.
Other Coordinates: cc_cap,
cc_cen, cc_coun,
cc_dupl, cc_equ,
cc_inst, cc_iucn,
cc_outl, cc_sea,
cc_urb, cc_val,
cc_zero
x <- data.frame(species = "A", decimallongitude = c(12.58, 12.58), decimallatitude = c(55.67, 30.00)) cc_gbif(x)#>#>#> species decimallongitude decimallatitude #> 2 A 12.58 30cc_gbif(x, value = "flagged")#>#>#> 1 2 #> FALSE TRUE